dplyr::if_else()

This allows you to make a binary conversion.

For example if_else(condition, true, false)
* success = if_else(epa > 0, 1, 0)
* color = if_else(posteam == "PIT", "yellow", "grey)

scale_color_identity()

This is useful in combination with the above example of assigning color in a plot, essentially it will take the “yellow” or “grey” argument automatically.

scale_color_manual()

This allows you to specify colors of interest like scale_color_manual(values = c("red", "black"))

forcats::reorder()

This allows you to reorder levels of a ggplot by another variable.

eg reorder(posteam, epa)

teamcolors package

Gives you ALL the colors for NFL teams

Load all the libraries you need

There are a few packages I will use in this guide, most of them related to data viz.

library(tidyverse) # Data Cleaning, manipulation, summarization, plotting
library(gt) # beautiful tables
library(DT) # beautiful interactive tables
library(ggthemes) # custom pre-built themes
library(bbplot) # more themes
library(ggtext) # custom text color
library(teamcolors) # NFL team colors and logos
library(ggforce) # better annotations
library(ggridges) # many distributions at once
library(ggrepel) # better labels
library(ggbeeswarm) # beeswarm plots
library(waffle) # waffle plots

Read in the pbp data

This is taken almost verbatim from Ben’s Tutorial, but the idea is that you are adjusting the dataset to be ready for analysis. If you are interested in plays beyond pass/rush then you should probably NOT do these steps.

pbp <- read_csv("https://raw.githubusercontent.com/ryurko/nflscrapR-data/master/play_by_play_data/regular_season/reg_pbp_2018.csv")

# clean up the data for further analysis
pbp_rp <- pbp %>%
  # grab only penalties, pass, and run plays
  filter(!is.na(epa), play_type == "no_play" | play_type == "pass" | play_type == "run") %>%
  # create pass, rush and success columns
  mutate(
    pass = if_else(str_detect(desc, "(pass)|(sacked)|(scramble)"), 1, 0),
    rush = if_else(str_detect(desc, "(left end)|(left tackle)|(left guard)|(up the middle)|(right guard)|(right tackle)|(right end)") & pass == 0, 1, 0),
    success = ifelse(epa > 0, 1, 0)
  ) %>%
  # filter to only pass or rush plays
  filter(pass == 1 | rush == 1) %>%
  mutate(
    passer_player_name = ifelse(play_type == "no_play" & pass == 1,
      str_extract(desc, "(?<=\\s)[A-Z][a-z]*\\.\\s?[A-Z][A-z]+(\\s(I{2,3})|(IV))?(?=\\s((pass)|(sack)|(scramble)))"),
      passer_player_name
    ),
    receiver_player_name = ifelse(play_type == "no_play" & str_detect(desc, "pass"),
      str_extract(
        desc,
        "(?<=to\\s)[A-Z][a-z]*\\.\\s?[A-Z][A-z]+(\\s(I{2,3})|(IV))?"
      ),
      receiver_player_name
    ),
    rusher_player_name = ifelse(play_type == "no_play" & rush == 1,
      str_extract(desc, "(?<=\\s)[A-Z][a-z]*\\.\\s?[A-Z][A-z]+(\\s(I{2,3})|(IV))?(?=\\s((left end)|(left tackle)|(left guard)|      (up the middle)|(right guard)|(right tackle)|(right end)))"),
      rusher_player_name
    )
  ) %>%
  mutate(
    name = if_else(!is.na(passer_player_name), passer_player_name, rusher_player_name),
    rusher = rusher_player_name,
    receiver = receiver_player_name,
    play = 1
  )

Themes

ggplot2 out of the box comes with a bunch of themes, things like theme_bw(), theme_minimal(), theme_classic(), and the default theme_grey().

Let’s see what they look like with the same plot as above.

theme_bw()

• Notice that we now have grey gridlines, a black border, and a white background.

theme_minimal()
- Notice that we still have grey gridlines, a white background, but now no black border.

theme_classic()

• Notice that we now have NO gridlines, a half black border, and the same white background.

But as with almost everything in R, there are more packages that add more functionality! In this case, there are entire packages dedicated to themes in ggplot2 and you have the ability to build your own themes!

More themes

library(ggthemes)
library(bbplot)

The ggthemes package gives you a wide assortment of additional themes as seen here. Most importantly it also gives you ideas about customizations to your personal theme. If you parse through the source code, you can create your own theme and utilize across your visualizations.

theme_fivethirtyeight()

• The difference from theme_minimal() is ironically, minimal but the main difference is heavier grey gridlines, and a subtle grey background - which aligns with the FiveThirtyEight style.

Again, the exciting part about ggthemes in my mind is the concept of creating your own theme. In fact, the code for this theme is pretty simple!

theme_fivethirtyeight <- function(base_size = 12, base_family = "sans") {
  colors <- deframe(ggthemes::ggthemes_data[["fivethirtyeight"]])
  (theme_foundation(base_size = base_size, base_family = base_family)
  + theme(
      line = element_line(colour = "black"),
      rect = element_rect(
        fill = colors["Light Gray"],
        linetype = 0, colour = NA
      ),
      text = element_text(colour = colors["Dark Gray"]),
      axis.title = element_blank(),
      axis.text = element_text(),
      axis.ticks = element_blank(),
      axis.line = element_blank(),
      legend.background = element_rect(),
      legend.position = "bottom",
      legend.direction = "horizontal",
      legend.box = "vertical",
      panel.grid = element_line(colour = NULL),
      panel.grid.major =
        element_line(colour = colors["Medium Gray"]),
      panel.grid.minor = element_blank(),
      plot.title = element_text(hjust = 0, size = rel(1.5), face = "bold"),
      plot.margin = unit(c(1, 1, 1, 1), "lines"),
      strip.background = element_rect()
    ))
}

Edited Theme

I personally edited this so that it didn’t remove axis titles, and to have a white background instead of gray, which you can see below.

theme_538 <- function(base_size = 12, font = "Lato") {

  # Text setting
  txt <- element_text(size = base_size + 2, colour = "black", face = "plain")
  bold_txt <- element_text(
    size = base_size + 2, colour = "black",
    family = "Montserrat", face = "bold"
  )
  large_txt <- element_text(size = base_size + 4, color = "black", face = "bold")


  theme_minimal(base_size = base_size, base_family = font) +
    theme(
      # Legend Settings
      legend.key = element_blank(),
      legend.background = element_blank(),
      legend.position = "bottom",
      legend.direction = "horizontal",
      legend.box = "vertical",

      # Backgrounds
      strip.background = element_blank(),
      strip.text = large_txt,
      plot.background = element_rect(),
      plot.margin = unit(c(1, 1, 1, 1), "lines"),

      # Axis & Titles
      text = txt,
      axis.text = txt,
      axis.ticks = element_blank(),
      axis.line = element_blank(),
      axis.title = bold_txt,
      plot.title = large_txt,

      # Panel
      panel.grid = element_line(colour = NULL),
      panel.grid.major = element_line(colour = "#D2D2D2"),
      panel.grid.minor = element_blank(),
      panel.background = element_blank(),
      panel.border = element_blank()
    )
}

Regardless - the idea here is that you can:

• Use a built in theme (theme_bw, theme_minimal, etc)
  
• Use a pre-built theme (bbplot, ggthemes, etc)
  
• Or build your own theme!

All are valid, but you don’t necessarily have to actually manually code the theme element changes to each and every plot. You can at the least write your own theme as a function and use it. Alternatively, you can write your own package (easier than it sounds!) and source that.

If you would like to read more about customizing your OWN theme - check out the great resource by Simon Jackson at his blog.

Let’s improve this a bit.

wr_duel_plot <- ggplot(
  wr_duel,
  aes(x = game_date, y = mean_epa, color = receiver)
) +
  geom_line(size = 1) +
  theme_538() +
  geom_hline(yintercept = 0, size = 1, color = "black") +
  labs(
    x = "\nGame Date",
    y = "EPA (Average)",
    title = "Quick comparison of AB vs Juju across the 2018 season",
    caption = "Data: @nflscrapR"
  )

wr_duel_plot

Change the colors

But we can still improve this a lot - it feels a bit crowded, plus the red/blue colro scheme doesn’t align with the team’s color or anything else. We can add colored text via the ggtext package, or we can manually change the colors. Also note that you can grab the team’s colors via teamcolors package.

pit_colors <- teamcolors %>% 
  filter(name == "Pittsburgh Steelers") %>%
  select(name:secondary)

pit_colors

## # A tibble: 1 x 4
##   name                league primary secondary
##   <chr>               <chr>  <chr>   <chr>    
## 1 Pittsburgh Steelers nfl    #000000 #ffb612

pit_primary <- pull(pit_colors, primary)
pit_secondary <- pull(pit_colors, secondary)

Asign the colors

wr_duel_plot <- ggplot(wr_duel,
               aes(x = game_date, y = mean_epa, 
                   color = if_else(receiver == "A.Brown", pit_primary, pit_secondary))) +
  geom_line(size = 1) +
  theme_538() +
  geom_hline(yintercept = 0, size = 1, color = "black") +
  labs(x = "",
       y = "EPA (Average)",
       title = "Quick comparison of <span style='color:#000000'>**AB**</span> vs <span style='color:#ffb612'>**Juju**</span> across the 2018 season",
       caption = "Data: @nflscrapR") +
  scale_color_identity() +
  theme(plot.title = element_markdown())

wr_duel_plot

Add the legend back

Alternatively, if you didn’t want to drop a legend, you could approach it this way.

wr_duel_plot <- ggplot(
  wr_duel,
  aes(
    x = game_date, y = mean_epa,
    color = receiver
  )
) +
  geom_line(size = 1) +
  theme_538() +
  geom_hline(yintercept = 0, size = 1, color = "black") +
  labs(
    x = "",
    y = "EPA (Average)",
    title = "Quick comparison of AB vs Juju across the 2018 season",
    caption = "Data: @nflscrapR"
  ) +
  scale_color_manual(values = c(pit_primary, pit_secondary)) +
  theme(
    legend.title = element_blank(),
    legend.position = c(0.2, 0.1)
  )

wr_duel_plot

Or try direct labeling!

wr_duel_plot +
  theme(legend.position = "none") +
  geom_text(data = filter(wr_duel, game_date == "2018-09-09"),
            aes(x = game_date, y = mean_epa, label = receiver),
            hjust = 0, nudge_y = 0.1, size = 4
  ) +
  geom_point(data = filter(wr_duel, game_date == "2018-09-09"), 
             size = 3
  )

More than a linear time series

You could also try out a connected line plot which lets you plot an x and y axis, then assign “time” as a 3rd variable. I find that it helps to add a connecting line, or else you may have trouble following the linear change in time.

# Prepare data
juju_do_it <- pbp_rp %>%
  filter(receiver == "J.Smith-Schuster") %>%
  arrange(desc(game_date)) %>%
  group_by(game_date) %>%
  summarize(
    total_yards = sum(yards_gained, na.rm = TRUE),
    total_airyards = sum(air_yards, na.rm = TRUE)
  ) %>%
  head(5) %>%
  mutate(
    game_num = row_number(),
    game_text = glue::glue("Game {game_num}")
  )

ggplot(juju_do_it, aes(x = total_airyards, y = total_yards, color = game_num)) +
  geom_point(size = 5) +
  # geom path follows the order of underlying data
  geom_path(size = 2) +
  # creates a line for comparison
  geom_abline(intercept = 0, slope = 1, color = "grey", linetype = "dashed") +
  # adds labels to only game 1 and 5
  geom_text(
    data = filter(juju_do_it, game_num %in% c(1, 5)),
    aes(label = game_text),
    hjust = 1, nudge_x = -5
  ) +
  # set scales for 0-axis
  scale_x_continuous(limits = c(0, 140)) +
  scale_y_continuous(limits = c(0, 140)) +
  # change color gradient to start at black and transition to yellow
  scale_color_gradient(low = pit_primary, high = pit_secondary) +
  theme_538() +
  labs(
    x = "\nTotal Air Yards",
    y = "Total Yards\n",
    title = "Even with his highest Air Yardage, Juju struggled in Game 4",
    caption = "Data: @nflscrapR"
  ) +
  theme(legend.position = "none")

Notice that the above plot has a diagonal trend line that runs intersecting at 0 with a slope of 1:

geom_abline(intercept = 0, slope = 1, color = "grey", linetype = "dashed")

This basic call can be applied to lots of different plots to give a reference line, where you can separate plays/players/teams into above/below the line.

Facets

rb_trio_plot <- rb_trio %>%
  ggplot(aes(x = player, y = mean_yards, fill = player, position = "dodge", group = play_type)) +
  geom_col() +
  facet_grid(~play_type)

rb_trio_plot

Now we are adding color by player and separating into small multiples or facets that represent the play type. Any categorical variable could be used in this fashion - you could essentially build the plot 1x and then facet by a factor to generate N versions of that graph all plotted together.

Let’s raise the bar

rb_trio_plot +
  geom_hline(yintercept = 0.03, color = "black", size = 2) +
  theme_538() +
  scale_fill_manual(values = c(pit_primary, pit_secondary, "grey")) +
  labs(
    x = "",
    y = "Avg Yards per Play",
    title = "Conner and Samuels were interchangeable in 2018",
    subtitle = "Ridley is no longer on the team",
    caption = "Data: @nflscrapR"
  ) +
  theme(
    panel.grid.major.x = element_blank(),
    panel.grid.major.y = element_line(color = "white", size = 1),
    panel.ontop = TRUE,
    legend.position = "none"
  )  +
  scale_y_continuous(
    breaks = seq(0, 6, 1)
  )

Or keep it more traditional

rb_trio_plot +
  geom_hline(yintercept = 0, color = "black", size = 2) +
  theme_538() +
  scale_fill_manual(values = c(pit_primary, pit_secondary, "grey")) +
  labs(
    x = "",
    y = "Avg Yards per Play",
    title = "Conner and Samuels were interchangeable in 2018",
    subtitle = "Ridley is no longer on the team",
    caption = "Data: @nflscrapR"
  ) +
  theme(
    panel.grid.major.x = element_blank(),
    legend.position = "none"
  ) +
  scale_y_continuous(
    breaks = seq(0, 6, 1)
  )

Flip the bar

epa_play <- pbp_rp %>% 
  filter(pass == 1) %>% 
  group_by(posteam) %>% 
  summarize(
    n = n(),
    epa_per_db = sum(epa, na.rm = TRUE) / n,
    success_rate = sum(epa) / n
  )

epa_play %>% 
  ggplot(aes(x = posteam, y = epa_per_db)) +
  geom_col()

This could be a useful summary, but there’s a few issues.

• Teams arranged by alphabetical name, which is not that useful
  
• x-axis is hard to read (worse so if you had full team names)

So let’s try rotating the bar plot.

epa_play %>%
  ggplot(aes(x = epa_per_db, y = reorder(posteam, epa_per_db), )) +
  geom_col()

Yikes - that is not what we want! Instead of just swapping the x and y axes, we should have used coord_flip() - this will actually rotate the plot rather than change the structure.

epa_play %>%
  ggplot(aes(x = reorder(posteam, epa_per_db), y = epa_per_db)) +
  geom_col(aes(fill = if_else(epa_per_db >= 0, "green", "red"))) +
  coord_flip() +
  scale_fill_identity()

Now this is more readable, clearly arranged by the strong passing vs weak passing teams, but still could be improved. Namely, red/green is not ideal for color-blindness, and the default red/green are pretty abrasively bright! However, we can still improve the grid lines (don’t need horizontal), add some better labels, and finish out the plot.

epa_play %>%
  ggplot(aes(x = reorder(posteam, epa_per_db), y = epa_per_db)) +
  geom_col(aes(fill = if_else(epa_per_db >= 0, "#2c7bb6", "#d7181c"))) +
  coord_flip() +
  scale_fill_identity() +
  theme_538() +
  theme(panel.grid.major.y = element_blank()) +
  geom_hline(yintercept = 0) +
  scale_y_continuous(breaks = seq(-0.2, 0.3, 0.1)) +
  labs(
    x = "",
    y = "EPA per Dropback",
    title = "The majority of teams had positive EPA/dropback",
    subtitle = "But there are some clear outliers",
    caption = "Data: @nflscrapR"
  )

Bar plot alternatives

There are some alternative reproducible methods for various bar plots on one of my other guides.

epa_play %>%
  ggplot(aes(x = reorder(posteam, epa_per_db), y = epa_per_db)) +
  geom_col(aes(fill = if_else(epa_per_db >= 0, "#2c7bb6", "#d7181c")),
    width = 0.2
  ) +
  geom_point(aes(color = if_else(epa_per_db >= 0, "#2c7bb6", "#d7181c")),
    size = 5
  ) +
  coord_flip() +
  scale_fill_identity(aesthetics = c("fill", "colour")) +
  theme_538() +
  theme(panel.grid.major.y = element_blank()) +
  geom_hline(yintercept = 0) +
  scale_y_continuous(breaks = seq(-0.2, 0.3, 0.1)) +
  labs(
    x = "",
    y = "EPA per Dropback",
    title = "The majority of teams had positive EPA/dropback",
    subtitle = "But there are some clear outliers",
    caption = "Data: @nflscrapR"
  )

epa_play %>%
  ggplot(aes(x = reorder(posteam, epa_per_db), y = epa_per_db)) +
  geom_col(aes(fill = if_else(epa_per_db >= 0, "#2c7bb6", "#d7181c"))) +
  geom_text(aes(
    label = posteam,
    color = if_else(epa_per_db >= 0, "#2c7bb6", "#d7181c"),
    hjust = if_else(epa_per_db > 0, -0.1, 1.1)
  )) +
  coord_flip() +
  scale_fill_identity(aesthetics = c("fill", "colour")) +
  theme_538() +
  theme(
    panel.grid.major.y = element_blank(),
    axis.text.y = element_blank()
  ) +
  geom_hline(yintercept = 0) +
  scale_y_continuous(breaks = seq(-0.2, 0.3, 0.1)) +
  labs(
    x = "",
    y = "EPA per Dropback",
    title = "The majority of teams had positive EPA/dropback",
    subtitle = "But there are some clear outliers",
    caption = "Data: @nflscrapR"
  )

epa_play %>%
  ggplot(aes(x = reorder(posteam, epa_per_db), y = epa_per_db)) +
  geom_point(aes(color = if_else(epa_per_db >= 0, "#2c7bb6", "#d7181c")),
    size = 3
  ) +
  geom_text(aes(
    label = posteam,
    color = if_else(epa_per_db >= 0, "#2c7bb6", "#d7181c"),
    hjust = if_else(epa_per_db > 0, -0.2, 1.2)
  )) +
  coord_flip() +
  scale_fill_identity(aesthetics = c("fill", "colour")) +
  theme_538() +
  theme(
    panel.grid.major.y = element_blank(),
    axis.text.y = element_blank()
  ) +
  geom_hline(yintercept = 0) +
  scale_y_continuous(breaks = seq(-0.2, 0.3, 0.1)) +
  labs(
    x = "",
    y = "EPA per Dropback",
    title = "The majority of teams had positive EPA/dropback",
    subtitle = "But there are some clear outliers",
    caption = "Data: @nflscrapR"
  )

Basic Scatterplot

Basic form:

• ggplot(aes(x = value, y = other_value)) + geom_point()

qb_success_rate <- qbs %>%
  ggplot(aes(x = success_per_play, y = epa_per_play)) +
  geom_point() +
  labs(x = "Success rate",
       y = "EPA per play",
       caption = "Data from nflscrapR",
       title = "QB success rate and EPA/play",
       subtitle = "2018, min 100 pass attempts, includes all QB's rush and pass plays") +
  theme_bw() +
  theme(axis.title = element_text(size = 12),
        axis.text = element_text(size = 10),
        plot.title = element_text(size = 16, hjust = 0.5),
        plot.subtitle = element_text(size = 14, hjust = 0.5),
        plot.caption = element_text(size = 12))

qb_success_rate

This is a nice plot, minorly scaled back from Ben’s example code. There is a clear linear relationship between succcess rate (EPA > 0) and EPA per Play, which makes sense.

Add reference lines

We could add back in a few of Ben’s code examples to improve it.

qb_success_rate +
  geom_hline(yintercept = mean(qbs$epa_per_play), color = "red", linetype = "dashed") +
  geom_vline(xintercept =  mean(qbs$success_per_play), color = "red", linetype = "dashed")

This adds lines at the averages for each axis to help with comparison.

We could also accomplish this with the code below. In the below example, it is initially more verbose but also gives you a saved data point to work with, and could be useful if for example you wanted to do a group_by summary or a filter, basically anything beyond just a pure mean.

qb_epa_per_play <- qbs %>%
  summarize(mean = mean(epa_per_play)) %>%
  pull(mean)

qb_success_per_play <- qbs %>%
  summarize(mean = mean(success_per_play)) %>%
  pull(mean)

qb_success_rate +
  geom_hline(yintercept = qb_epa_per_play, color = "red", linetype = "dashed") +
  geom_vline(xintercept = qb_success_per_play, color = "red", linetype = "dashed")

Add linear trendline

We could also add a linear trendline to this plot. Either method shown below is valid, but stat_smooth allows for some additional customization.

qb_success_rate +
  stat_smooth(method = "lm", geom = "line", alpha = 0.5, se = FALSE, color = "red", size = 1)

qb_success_rate +
  geom_smooth(method = "lm", se = FALSE, color = "red")

More than 2 Variables

Now Ben has 2x variables assigned as aesthetics in this plot, success rate as X, EPA/play as Y.

He also added a 3rd variable (size) as an aesthetic. Importantly, because we are putting size and color INSIDE aes() we get to use traditional tidyverse evaluation, so we can reference columns directly, like you see with n_plays and posteam.

qbs %>%
  ggplot(
    aes(x = success_per_play, y = epa_per_play)
  ) +
  # Notice that color/size inside aes()
  geom_point(
    aes(
      color = if_else(posteam == "SF", "red", "black"),
      size = n_plays / 60
    ),
    alpha = 0.50
  ) +
  # we need this to assign red/black to the actual color
  scale_color_identity() +
  labs(
    x = "Success rate",
    y = "EPA per play",
    caption = "Data from nflscrapR",
    title = "QB success rate and EPA/play",
    subtitle = "2018, min 100 pass attempts, includes all QB's rush and pass plays"
  ) +
  theme_bw() +
  theme(
    axis.title = element_text(size = 12),
    axis.text = element_text(size = 10),
    plot.title = element_text(size = 16, hjust = 0.5),
    plot.subtitle = element_text(size = 14, hjust = 0.5),
    plot.caption = element_text(size = 12)
  ) +
  theme(legend.position = "none")

Add labels

We can then add nice labels to ALL the players via ggrepel which automatically repels labels so there is minimal to no overlap.

qbs %>%
  ggplot(aes(x = success_per_play, y = epa_per_play)) +
  # Notice that color/size inside aes()
  geom_point(aes(color = if_else(posteam == "SF", "red", "black"), size = n_plays / 60), alpha = 0.50) +
  # we need this to assign red/black to the actual color
  scale_color_identity() +

  # add labels for all players
  geom_text_repel(aes(label = name, color = if_else(posteam == "SF", "red", "black")),
    force = 1, point.padding = 0.1,
    segment.size = 0.2
  ) +
  labs(
    x = "Success rate",
    y = "EPA per play",
    caption = "Data from nflscrapR",
    title = "QB success rate and EPA/play",
    subtitle = "2018, min 100 pass attempts, includes all QB's rush and pass plays"
  ) +
  theme_bw() +
  theme(
    axis.title = element_text(size = 12),
    axis.text = element_text(size = 10),
    plot.title = element_text(size = 16, hjust = 0.5),
    plot.subtitle = element_text(size = 14, hjust = 0.5),
    plot.caption = element_text(size = 12)
  ) +
  theme(legend.position = "none")

qbs %>%
  ggplot(aes(x = success_per_play, y = epa_per_play)) +
  # Notice that color/size inside aes()
  geom_point(aes(color = if_else(posteam == "SF", "red", "black"), size = n_plays / 60), alpha = 0.50) +
  # we need this to assign red/black to the actual color
  scale_color_identity() +

  # add labels JUST for SF
  geom_text_repel(
    data = filter(qbs, posteam == "SF"),
    aes(label = name), color = "red",
    force = 1, point.padding = 0.1,
    segment.size = 0.2
  ) +
  labs(
    x = "Success rate",
    y = "EPA per play",
    caption = "Data from nflscrapR",
    title = "QB success rate and EPA/play",
    subtitle = "2018, min 100 pass attempts, includes all QB's rush and pass plays"
  ) +
  theme_bw() +
  theme(
    axis.title = element_text(size = 12),
    axis.text = element_text(size = 10),
    plot.title = element_text(size = 16, hjust = 0.5),
    plot.subtitle = element_text(size = 14, hjust = 0.5),
    plot.caption = element_text(size = 12)
  ) +
  theme(legend.position = "none")

Add annotations

Staying with our San Francisco example, we can also go about this process differently to answer how did Jimmy G. and Nick the Mullet compare? We can add nice annotations via the ggforce package for just the two players of interest.

qbs %>%
  ggplot(aes(x = success_per_play, y = epa_per_play)) +
  # Notice that color/size inside aes()
  geom_point(aes(
    color = if_else(posteam == "SF", "red", "black"),
    size = n_plays / 60
  ),
  alpha = 0.50
  ) +
  # we need this to assign red/black to the actual color
  scale_color_identity() +
  # add labels JUST for Mullens/Garoppolo with ggforce
  geom_mark_hull(
    aes(
      filter = name %in% c("J.Garoppolo", "N.Mullens"),
      description = "Mullens + Garoppolo performed similarly in 2018"
    ),
    color = "red", label.fontface = "bold", label.colour = "red", con.colour = "red"
  ) +
  labs(
    x = "Success rate",
    y = "EPA per play",
    caption = "Data from nflscrapR",
    title = "QB success rate and EPA/play",
    subtitle = "2018, min 100 pass attempts, includes all QB's rush and pass plays"
  ) +
  theme_bw() +
  theme(
    axis.title = element_text(size = 12),
    axis.text = element_text(size = 10),
    plot.title = element_text(size = 16, hjust = 0.5),
    plot.subtitle = element_text(size = 14, hjust = 0.5),
    plot.caption = element_text(size = 12)
  ) +
  theme(legend.position = "none")

Histogram

The basic idea of a histogram is that the data is binned along some range (2 airyards in below example) across the x axis, and all values of x that fall within this count add to the total count for that specific bin.

Basic form:

• ggplot(aes(x = value)) + geom_histogram()
  • Notice NO y-value as it is generated automatically via the plot

Let’s take a look at KC and SEA, teams with very different approaches to their offenses.

sea_color <- teamcolors %>%
  filter(name == "Seattle Seahawks") %>%
  pull(primary)

kc_color <- teamcolors %>%
  filter(name == "Kansas City Chiefs") %>%
  pull(primary)

pbp_rp %>%
  filter(play_type != "no_play", posteam %in% c("SEA", "KC")) %>%
  group_by(posteam, play_type) %>%
  summarize(n = n()) %>%
  mutate(freq = n / sum(n))

## # A tibble: 4 x 4
## # Groups:   posteam [2]
##   posteam play_type     n  freq
##   <chr>   <chr>     <int> <dbl>
## 1 KC      pass        606 0.619
## 2 KC      run         373 0.381
## 3 SEA     pass        465 0.474
## 4 SEA     run         515 0.526

So KC threw the ball almost 62% of the time, while Sea only threw the ball about 47% of the time!

But what does the distribution of throws look like between KC and SEA?

pbp_rp %>%
  filter(play_type == "pass") %>%
  filter(posteam %in% c("SEA", "KC")) %>%
  ggplot(aes(x = air_yards, fill = posteam)) +
  geom_histogram(binwidth = 2)

## Warning: Removed 79 rows containing non-finite values (stat_bin).

The basic histogram is “fine” but let’s spruce it up a bit! We can add our theme, the team colors, and some better labels.

pbp_rp %>%
  filter(play_type == "pass") %>%
  filter(posteam %in% c("SEA", "KC")) %>%
  ggplot(aes(x = air_yards, fill = posteam)) +
  geom_histogram(binwidth = 2, alpha = 0.9) +
  scale_fill_manual(values = c(kc_color, sea_color)) +
  geom_hline(yintercept = 0, size = 1) +
  theme_538() +
  theme(
    legend.title = element_blank(),
    legend.position = c(0.6, 0.9)
  ) +
  scale_x_continuous(breaks = seq(-10, 60, 10)) +
  labs(
    x = "\nAir Yards",
    y = "Count",
    title = "KC threw more passes at all ranges",
    caption = "Data: @nflscrapR"
  )

## Warning: Removed 79 rows containing non-finite values (stat_bin).

Density Plot

“Computes and draws kernel density estimate, which is a smoothed version of the histogram. This is a useful alternative to the histogram for continuous data that comes from an underlying smooth distribution.” - ggplot2 docs

Basic form:

• ggplot(aes(x = value)) + geom_density()
  • Again notice NO y-value, it is generated automatically

pbp_rp %>%
  filter(play_type == "pass") %>%
  filter(posteam %in% c("SEA", "KC")) %>%
  ggplot(aes(x = air_yards, fill = posteam)) +
  geom_density(alpha = 0.8) +
  scale_fill_manual(values = c(kc_color, sea_color)) +
  theme_538() +
  theme(
    legend.title = element_blank(),
    legend.position = c(0.6, 0.9)
  ) +
  scale_x_continuous(breaks = seq(-10, 60, 10))

## Warning: Removed 79 rows containing non-finite values (stat_density).

An important point - I try not to focus on the Y axis for either histogram/density plots as we are looking at the distribution itself rather than specific numbers. You can scale out the y-axis in a few ways for density plots, which I’ll demonstrate below.

pbp_rp %>%
  filter(play_type == "pass") %>%
  filter(posteam %in% c("SEA", "KC")) %>%
  ggplot(aes(x = air_yards, y = ..scaled.., fill = posteam)) +
  geom_density(alpha = 0.8) +
  scale_fill_manual(values = c(kc_color, sea_color)) +
  theme_538() +
  theme(
    legend.title = element_blank(),
    legend.position = c(0.6, 0.9)
  ) +
  scale_x_continuous(breaks = seq(-10, 60, 10))

## Warning: Removed 79 rows containing non-finite values (stat_density).

Interestingly though, we see that KC and SEA essentially attacked the field in the same way, BUT SEA threw so many fewer passes which was captured in the histogram.

pbp_rp %>%
  filter(play_type == "pass") %>%
  filter(posteam %in% c("SEA", "KC")) %>%
  ggplot(aes(x = air_yards, y = posteam, fill = posteam)) +
  geom_density_ridges() +
  scale_fill_manual(values = c(kc_color, sea_color)) +
  theme_538() +
  theme(
    panel.grid.major.y = element_blank(),
    legend.position = "none"
  ) +
  scale_x_continuous(breaks = seq(-10, 60, 10)) +
  labs(
    x = "Air Yards",
    y = "",
    title = "SEA and KC pass to similar depths of the field",
    caption = "Data: @nflscrapR"
  )

## Picking joint bandwidth of 2.53

## Warning: Removed 79 rows containing non-finite values
## (stat_density_ridges).

Boxplots

Boxplots are another way of showing central tendency + range of a distribution, but they can still have their quirks or difficulties in explanations. I typically find that adding a geom_jitter() call on top of the boxplot helps with showing both the distribution and the central tendency/range, but YMMV.

Basic form:

• ggplot(aes(x = category, y = value)) + geom_boxplot()

pbp_rp %>%
  filter(play_type == "pass") %>%
  filter(posteam %in% c("SEA", "KC")) %>%
  ggplot(aes(x = posteam, y = air_yards, fill = posteam)) +
  geom_boxplot() +
  geom_jitter(width = 0.2, alpha = 0.2) +
  scale_fill_manual(values = c(kc_color, sea_color)) +
  theme_538() +
  theme(legend.position = "none")

## Warning: Removed 79 rows containing non-finite values (stat_boxplot).

## Warning: Removed 79 rows containing missing values (geom_point).

pbp_rp %>%
  filter(play_type != "no_play", posteam %in% c("SEA", "KC")) %>% 
  ggplot(aes(x = play_type, y = epa , fill = play_type)) +
  geom_boxplot() +
  geom_jitter(width = 0.3, alpha = 0.1) +
  scale_fill_manual(values = c(kc_color, sea_color)) +
  theme_538() +
  theme(legend.position = "none") +
  facet_grid(~posteam)

Sina plot

geom_sina() from the ggforce package is an alternative to the above wokflow, and is somewhat similar to a vertical geom_density()

Basic form:

• ggplot(aes(x = category, y = value)) + geom_sina()

pbp_rp %>%
  filter(play_type != "no_play", posteam %in% c("SEA", "KC")) %>%
  ggplot(aes(x = play_type, y = epa, color = posteam)) +
  geom_sina(alpha = 0.5) +
  scale_fill_manual(values = c(kc_color, sea_color), aesthetics = c("fill", "color")) +
  theme_538() +
  theme(legend.position = "none") +
  facet_grid(~posteam)

Beeswarm

geom_beeswarm() from the beeswarm package is an alternative to the above wokflow, but is almost identical to geom_sina() in its basic form.

Basic form:

• ggplot(aes(x = category, y = value)) + geom_beeswarm()

library(ggbeeswarm)

pbp_rp %>%
  filter(play_type != "no_play", posteam %in% c("SEA", "KC")) %>%
  ggplot(
    aes(x = play_type, y = epa, color = posteam)
  ) +
  geom_beeswarm(alpha = 0.5) +
  scale_fill_manual(values = c(kc_color, sea_color), aesthetics = c("fill", "color")) +
  theme_538() +
  theme(legend.position = "none") +
  facet_grid(~posteam)

Importantly, although this looks very similar to the geom_sina() plots, you have more options about customizing the “swarming”.

pbp_rp %>%
  filter(play_type != "no_play", posteam %in% c("SEA", "KC")) %>%
  ggplot(
    aes(x = play_type, y = epa, color = posteam)
  ) +
  geom_beeswarm(priority = "random", alpha = 0.5, size = 0.5) +
  scale_fill_manual(values = c(kc_color, sea_color), aesthetics = c("fill", "color")) +
  theme_538() +
  theme(legend.position = "none") +
  facet_grid(~posteam)

Dumbbell Plots

Dumbell plots are typically best served comparing two summary numbers within a group.

Basic form:

• ggplot(aes(x = value, y = category, group = group)) + geom_line() + geom_point() + coord_flip()

rush_v_pass <- pbp_rp %>% 
  filter(play_type != "no_play", penalty == 0) %>% 
  group_by(play_type, posteam) %>% 
  summarize(avg_yds = mean(yards_gained, na.rm = TRUE)) %>% 
  ungroup()

nfl_rvp <- pbp_rp %>% 
  filter(play_type != "no_play") %>% 
  group_by(play_type) %>% 
  summarize(avg_yds = mean(yards_gained, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(posteam = "NFL")

rush_v_pass <- bind_rows(rush_v_pass, nfl_rvp) %>% 
  mutate(play_type = factor(play_type,
                            levels = c("pass", "run"),
                            labels = c("Pass", "Rush")))

rush_v_pass %>% 
  ggplot(aes(x = fct_rev(fct_reorder2(posteam, desc(play_type), avg_yds)), y = avg_yds, color = play_type)) +
  geom_line(aes(group = posteam), color = "grey", size = 3) +
  geom_point(size = 5) +
  coord_flip()

Adding some additional aesthetic changes to improve the graph:

• Color of the points
  
• Titles/Axes/Captions
  
• Direct label legend

rush_v_pass %>%
  ggplot(aes(x = fct_rev(fct_reorder2(posteam, desc(play_type), avg_yds)), y = avg_yds, color = play_type)) +
  geom_line(aes(group = posteam), color = "grey", size = 3) +
  geom_point(size = 5) +
  geom_text(
    data = filter(rush_v_pass, posteam == "KC" & play_type == "Pass"),
    aes(label = play_type),
    hjust = 0, nudge_y = 0.2, fontface = "bold", size = 6
  ) +
  geom_text(
    data = filter(rush_v_pass, posteam == "KC" & play_type == "Rush"),
    aes(label = play_type),
    hjust = 1, nudge_y = -0.2, fontface = "bold", size = 6
  ) +
  coord_flip() +
  scale_color_manual(values = c("#003399", "#ff2b4f")) +
  theme_538() +
  theme(
    panel.grid.major.y = element_blank(),
    legend.position = "none",
    axis.text.y = element_text(color = if_else(rush_v_pass$posteam == "NFL", "red", "black"))
  ) +
  labs(
    x = "",
    y = "\n Average Yards Gained",
    title = "Passing yards per play outperforms Rushing for all teams",
    caption = "Data: @nflscrapR"
  ) +
  scale_y_continuous(
    limits = c(3, 9),
    breaks = seq(3, 8, 1)
  )

Slope Chart

A slope chart allows you to show the change/trend between two points, most appropriately as a two point time series.

The basic form:

• ggplot(aes(x = time, y = value, group = group)) + geom_line() + geom_point()

This demo takes a bit of prep, you could skip this simply by using game_date instead of game_week, but I think it’s a useful code-chunk for you to have in the boolbox.

• case_when() is essentially a switch or a combination of if_else() statements.

We can use it to have a bunch of arguments where you can match some argument and then output something specific. Here we are checking if the game_date is between the two dates for that week’s games and then assigning game_week of the season.

game_num <- pbp_rp %>% 
  mutate(game_week = case_when(
    between(game_date, as.Date("2018-09-05"), as.Date("2018-09-11")) ~ 1,
    between(game_date, as.Date("2018-09-12"), as.Date("2018-09-18")) ~ 2,
    between(game_date, as.Date("2018-09-19"), as.Date("2018-09-25")) ~ 3,
    between(game_date, as.Date("2018-09-26"), as.Date("2018-10-02")) ~ 4,
    between(game_date, as.Date("2018-10-03"), as.Date("2018-10-09")) ~ 5,
    between(game_date, as.Date("2018-10-10"), as.Date("2018-10-16")) ~ 6,
    between(game_date, as.Date("2018-10-17"), as.Date("2018-10-23")) ~ 7,
    between(game_date, as.Date("2018-10-24"), as.Date("2018-10-30")) ~ 8,
    between(game_date, as.Date("2018-10-31"), as.Date("2018-11-06")) ~ 9,
    between(game_date, as.Date("2018-11-07"), as.Date("2018-11-13")) ~ 10,
    between(game_date, as.Date("2018-11-14"), as.Date("2018-11-20")) ~ 11,
    between(game_date, as.Date("2018-11-21"), as.Date("2018-11-27")) ~ 12,
    between(game_date, as.Date("2018-11-28"), as.Date("2018-12-04")) ~ 13,
    between(game_date, as.Date("2018-12-05"), as.Date("2018-12-11")) ~ 14,
    between(game_date, as.Date("2018-12-12"), as.Date("2018-12-18")) ~ 15,
    between(game_date, as.Date("2018-12-19"), as.Date("2018-12-25")) ~ 16,
    between(game_date, as.Date("2018-12-30"), as.Date("2019-01-01")) ~ 17,
    TRUE ~ 99
    )
  ) %>% 
  filter(game_week != 99)

Now we can clean up some of the factors for better printing and limit to KC, the most offensively efficient team in 2018. We’ll be looking at 1st Half vs 2nd Half Runs & Passes.

wk_rvp <- game_num %>%
  filter(play_type != "no_play", game_half %in% c("Half1", "Half2")) %>%
  mutate(game_half = if_else(game_half == "Half1", "1st Half", "2nd Half")) %>%
  group_by(posteam, game_half, game_week, play_type) %>%
  count() %>% 
  ungroup()

kc_rvp <- wk_rvp %>% 
  filter(posteam == "KC") %>% 
  mutate(game_num = if_else(game_week <=11, game_week, game_week - 1),
         play_type = if_else(play_type == "run", "Rush", "Pass"),
         game_text = glue::glue("Game {game_num}")
         )

kc_rvp

## # A tibble: 64 x 7
##    posteam game_half game_week play_type     n game_num game_text
##    <chr>   <chr>         <dbl> <chr>     <int>    <dbl> <glue>   
##  1 KC      1st Half          1 Pass         15        1 Game 1   
##  2 KC      1st Half          1 Rush         11        1 Game 1   
##  3 KC      1st Half          2 Pass         11        2 Game 2   
##  4 KC      1st Half          2 Rush         10        2 Game 2   
##  5 KC      1st Half          3 Pass         27        3 Game 3   
##  6 KC      1st Half          3 Rush         14        3 Game 3   
##  7 KC      1st Half          4 Pass         16        4 Game 4   
##  8 KC      1st Half          4 Rush         14        4 Game 4   
##  9 KC      1st Half          5 Pass         25        5 Game 5   
## 10 KC      1st Half          5 Rush         11        5 Game 5   
## # … with 54 more rows

Notice that we have nice text for game_half and play_type and a game_num variable. Let’s build the basic slope chart. You need SOME type of grouping variable as your 3rd variable eg (var1 = x, var2 = y, var3 = group).

kc_rvp %>% 
  ggplot(aes(x = game_half, y = n, group = game_num)) +
  geom_point() +
  geom_line() +
  facet_grid(~play_type)

This is interesting and shows the key feature of how does the trend of 1st Half vs 2nd Half Rush vs Pass look like.

However, we aren’t sure which games match to fewer first-half rushes vs more second-half rushes We need to assign a color variable in our aes() call. But first, let’s figure out the games where KC rushed more in the 1st half (aka ESTABLISH THE RUN) vs the 2nd half (REAP THE REWARDS OF ESTABLISHMENT).

kc_runs <- kc_rvp %>% 
  filter(play_type == "Rush") %>% 
  spread(game_half, n) %>%
  mutate(balance = if_else(`1st Half` >= `2nd Half`, "Ran More in 1st", "Ran More in 2nd")) %>% 
  gather(key = "game_half", value = "n", `1st Half`:`2nd Half`) %>% 
  select(posteam, game_num,game_half, balance)

kc_runs 

## # A tibble: 32 x 4
##    posteam game_num game_half balance        
##    <chr>      <dbl> <chr>     <chr>          
##  1 KC             1 1st Half  Ran More in 2nd
##  2 KC             2 1st Half  Ran More in 2nd
##  3 KC             3 1st Half  Ran More in 1st
##  4 KC             4 1st Half  Ran More in 1st
##  5 KC             5 1st Half  Ran More in 2nd
##  6 KC             6 1st Half  Ran More in 2nd
##  7 KC             7 1st Half  Ran More in 2nd
##  8 KC             8 1st Half  Ran More in 2nd
##  9 KC             9 1st Half  Ran More in 2nd
## 10 KC            10 1st Half  Ran More in 2nd
## # … with 22 more rows

kc_runs %>% 
  filter(balance == "Ran More in 1st") %>% 
  distinct(game_num)

## # A tibble: 4 x 1
##   game_num
##      <dbl>
## 1        3
## 2        4
## 3       11
## 4       15

So games 3, 4, 11 and 15 were the only games were they ran more or equal amounts in the 1st Half than the 2nd Half. Fun fact - KC went 12-4 last season, and went 2-2 in the games where they ran equally or more in the 1st Half than the 2nd Half.

kc_rvp %>%
  ggplot(
    aes(
      x = game_half, y = n, group = game_week, 
      color = if_else(game_num %in% c(3, 4, 11, 15), "red", "blue")
    )
  ) +
  geom_point() +
  geom_line() +
  geom_text_repel(
    data = filter(
      kc_rvp, game_num %in% c(3, 4, 11, 15),
      game_half == "2nd Half"
    ),
    aes(label = game_num)
  ) +
  facet_grid(~play_type) +
  scale_color_identity()

Ok now we can see that in 3/4 of the ONLY games where they “established” the run in the 1st half they ended up passing dramatically more in the 2nd half (4, 11, 15). Let’s add some more context and details.

kc_rvp %>%
  ggplot(
    aes(
      x = game_half, y = n, group = game_week, 
      color = if_else(game_num %in% c(3, 4, 11, 15), "#ff2b4f", "#003399")
    )
  ) +
  geom_point() +
  geom_vline(xintercept = c(1, 2), size = 2, color = "black", alpha = 0.5) +
  geom_line(size = 2) +
  geom_point(size = 5) +
  geom_text_repel(
    data = filter(
      kc_rvp, game_num %in% c(3, 4, 11, 15),
      game_half == "2nd Half"
    ),
    aes(label = game_text),
    direction = "y", nudge_x = 0.1, segment.size = 0.1, hjust = 0,
    size = 5, fontface = "bold"
  ) +
  facet_grid(~play_type) +
  scale_color_identity() +
  theme_538() +
  theme(panel.grid.major.x = element_blank()) +
  labs(x = "", y = "N of Plays\n",
       title = "In 3 of 4 games where KC established the run they ended up throwing more in the 2nd half",
       subtitle = "They went 2-2 in these games, and 10-2 in their other games",
       caption = "Data: @nflscrapR")

The big players here are manually changing colors and adding filtered data to add text labels for only the points of interest.

That’s all for this section - on to Tables!

gt

The gt package is essentially a grammar of tables, allowing you to quickly build out tables and output to RTF, HTML, or LaTeX.

Let’s do a quick analysis!

schotty

## # A tibble: 32 x 3
##    posteam mean_pass plays
##    <chr>       <dbl> <int>
##  1 SEA         0.369   320
##  2 JAX         0.435   276
##  3 TEN         0.441   263
##  4 BUF         0.452   219
##  5 BAL         0.458   299
##  6 ARI         0.466   236
##  7 NYJ         0.473   256
##  8 DET         0.482   299
##  9 WAS         0.485   239
## 10 CAR         0.491   281
## # … with 22 more rows

schotty %>%
  slice(1:5, 28:32) %>%
  gt()

schotty_gt <- schotty %>%
  slice(1:5, 28:32) %>%
  arrange(desc(mean_pass)) %>%
  mutate(play_focus = if_else(mean_pass >= .50, "Pass Heavy", "Run Heavy")) %>%
  group_by(play_focus) %>%
  gt()

schotty_gt

schotty_gt %>%
  fmt_percent(columns = vars(mean_pass), decimals = 1) %>%
  tab_header(
    title = "Percentage of Passes by teams on 1st/2nd Down in 1st Half",
    subtitle = "Win Prob between 20 & 80, excludes final 2 minutes of the half"
  ) %>%
  cols_label(
    posteam = "Player",
    mean_pass = "Pass %",
    plays = "Plays"
  ) %>%
  cols_align(
    align = "center"
  ) %>%
  tab_source_note(
    source_note = "Table: @thomas_mock | Data: @nflscrapR"
  )

# 2018 and pass plays
pass_2018 <- pbp_rp %>%
  filter(play_type == "pass", penalty == 0, sack == 0, qb_scramble == 0)

third_down_passes <- pass_2018 %>%
  filter(down == 3, ydstogo <= 10) %>%
  group_by(receiver_player_name) %>%
  mutate(converted = if_else(yards_gained > ydstogo, 1, 0)) %>%
  select(receiver_player_name, yards_gained, ydstogo, epa, converted) %>%
  summarise(
    mean_epa = mean(epa, na.rm = TRUE),
    mean_yardage = mean(yards_gained, na.rm = TRUE),
    mean_ydstogo = mean(ydstogo, na.rm = TRUE),
    n = n(),
    conv_rate = sum(converted) / n
  ) %>%
  ungroup() %>%
  arrange(desc(conv_rate))

rbs <- c(
  "A.Kamara", "J.White", "J.Conner", "C.McCaffrey", "S.Barkley", "E.Elliott",
  "J.Mixon", "T.Gurley", "D.Johnson", "M.Gordon"
)

wrs <- c(
  "D.Westbrook", "A.Humphries", "C.Kupp", "G.Tate", "D.Pettis", "J.Edelman",
  "C.Kupp", "W.Snead IV", "M.Sanu", "T.Lockett", "T.Gabriel", "S.Shepard", "C.Beasley"
)

tes <- c("T.Kelce", "Z.Ertz", "G. Kittle", "E.Engram", "J.Cook", "E.Ebron")

top_players <- c(rbs, wrs, tes)

third_conv_table <- third_down_passes %>% 
  filter(n >= 10) %>% 
  mutate(position = case_when(
    receiver_player_name %in% rbs ~ "RB",
    receiver_player_name %in% wrs ~ "WR",
    receiver_player_name %in% tes ~ "TE",
    TRUE ~ NA_character_
  ),
  position = factor(position, levels = c("RB", "WR", "TE"))
  ) %>%
  filter(receiver_player_name %in% top_players) %>% 
  select(receiver_player_name, conv_rate, n,  everything(), -mean_epa) %>% 
  group_by(position) %>% 
  arrange(desc(conv_rate)) %>% 
  ungroup() %>% 
  gt::gt(groupname_col = "position") 

third_conv_table

third_conv_table %>% 
  tab_header(
    title = "3rd Down Conversion Rates (Slot WR vs RB vs TE)",
    subtitle = "Yds to go <= 10, N of Plays >= 10"
  ) %>% 
  fmt_percent(.,
              columns = vars(conv_rate),
              decimals = 1
  ) %>% 
  fmt_number(
    columns = vars(mean_yardage, mean_ydstogo),
    decimals = 1
  ) %>% 
  cols_label(
    receiver_player_name = "Player",
    mean_yardage = "Yds Gained",
    mean_ydstogo = "Yds to Go",
    n = "Plays",
    conv_rate = "Conversion Rate"
  ) %>% 
  cols_align(
    align = "center"
  ) %>% 
  tab_source_note(
    source_note = "Table: @thomas_mock | Data: @nflscrapR"
  ) %>% 
  tab_footnote(
    footnote = "Average Yards",
    locations = cells_column_labels(
      columns = vars(mean_yardage, mean_ydstogo)
    )
  )